Overview

Dataset statistics

Number of variables19
Number of observations2240
Missing cells0
Missing cells (%)0.0%
Duplicate rows179
Duplicate rows (%)8.0%
Total size in memory332.6 KiB
Average record size in memory152.1 B

Variable types

Categorical10
Numeric9

Alerts

Dataset has 179 (8.0%) duplicate rowsDuplicates
Income is highly overall correlated with NumWebPurchases and 4 other fieldsHigh correlation
NumWebPurchases is highly overall correlated with Income and 3 other fieldsHigh correlation
NumCatalogPurchases is highly overall correlated with Income and 4 other fieldsHigh correlation
NumStorePurchases is highly overall correlated with Income and 3 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with Income and 1 other fieldsHigh correlation
Total_Spending is highly overall correlated with Income and 4 other fieldsHigh correlation
AcceptedCmp5 is highly overall correlated with Total_SpendingHigh correlation
AcceptedCmp3 is highly imbalanced (62.4%)Imbalance
AcceptedCmp4 is highly imbalanced (61.7%)Imbalance
AcceptedCmp5 is highly imbalanced (62.4%)Imbalance
AcceptedCmp1 is highly imbalanced (65.6%)Imbalance
AcceptedCmp2 is highly imbalanced (89.7%)Imbalance
Recency has 28 (1.2%) zerosZeros
NumDealsPurchases has 46 (2.1%) zerosZeros
NumWebPurchases has 49 (2.2%) zerosZeros
NumCatalogPurchases has 586 (26.2%) zerosZeros

Reproduction

Analysis started2023-04-28 18:10:48.723719
Analysis finished2023-04-28 18:11:06.913695
Duration18.19 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Education
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Graduation
1127 
Post Graduate
856 
Under Graduate
257 

Length

Max length14
Median length10
Mean length11.605357
Min length10

Characters and Unicode

Total characters25996
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPost Graduate

Common Values

ValueCountFrequency (%)
Graduation 1127
50.3%
Post Graduate 856
38.2%
Under Graduate 257
 
11.5%

Length

2023-04-28T21:11:07.021918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:07.176954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1127
33.6%
graduate 1113
33.2%
post 856
25.5%
under 257
 
7.7%

Most occurring characters

ValueCountFrequency (%)
a 4480
17.2%
t 3096
11.9%
r 2497
9.6%
d 2497
9.6%
G 2240
8.6%
u 2240
8.6%
o 1983
7.6%
n 1384
 
5.3%
e 1370
 
5.3%
i 1127
 
4.3%
Other values (4) 3082
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21530
82.8%
Uppercase Letter 3353
 
12.9%
Space Separator 1113
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4480
20.8%
t 3096
14.4%
r 2497
11.6%
d 2497
11.6%
u 2240
10.4%
o 1983
9.2%
n 1384
 
6.4%
e 1370
 
6.4%
i 1127
 
5.2%
s 856
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
G 2240
66.8%
P 856
 
25.5%
U 257
 
7.7%
Space Separator
ValueCountFrequency (%)
1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24883
95.7%
Common 1113
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4480
18.0%
t 3096
12.4%
r 2497
10.0%
d 2497
10.0%
G 2240
9.0%
u 2240
9.0%
o 1983
8.0%
n 1384
 
5.6%
e 1370
 
5.5%
i 1127
 
4.5%
Other values (3) 1969
7.9%
Common
ValueCountFrequency (%)
1113
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4480
17.2%
t 3096
11.9%
r 2497
9.6%
d 2497
9.6%
G 2240
8.6%
u 2240
8.6%
o 1983
7.6%
n 1384
 
5.3%
e 1370
 
5.3%
i 1127
 
4.3%
Other values (4) 3082
11.9%

Marital_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Not Single
1444 
Single
796 

Length

Max length10
Median length10
Mean length8.5785714
Min length6

Characters and Unicode

Total characters19216
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowNot Single
4th rowNot Single
5th rowNot Single

Common Values

ValueCountFrequency (%)
Not Single 1444
64.5%
Single 796
35.5%

Length

2023-04-28T21:11:07.318537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:07.475117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
single 2240
60.8%
not 1444
39.2%

Most occurring characters

ValueCountFrequency (%)
S 2240
11.7%
i 2240
11.7%
n 2240
11.7%
g 2240
11.7%
l 2240
11.7%
e 2240
11.7%
N 1444
7.5%
o 1444
7.5%
t 1444
7.5%
1444
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14088
73.3%
Uppercase Letter 3684
 
19.2%
Space Separator 1444
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2240
15.9%
n 2240
15.9%
g 2240
15.9%
l 2240
15.9%
e 2240
15.9%
o 1444
10.2%
t 1444
10.2%
Uppercase Letter
ValueCountFrequency (%)
S 2240
60.8%
N 1444
39.2%
Space Separator
ValueCountFrequency (%)
1444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17772
92.5%
Common 1444
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 2240
12.6%
i 2240
12.6%
n 2240
12.6%
g 2240
12.6%
l 2240
12.6%
e 2240
12.6%
N 1444
8.1%
o 1444
8.1%
t 1444
8.1%
Common
ValueCountFrequency (%)
1444
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 2240
11.7%
i 2240
11.7%
n 2240
11.7%
g 2240
11.7%
l 2240
11.7%
e 2240
11.7%
N 1444
7.5%
o 1444
7.5%
t 1444
7.5%
1444
7.5%

Income
Real number (ℝ)

Distinct1975
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52237.975
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:07.622722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile19101.05
Q135538.75
median51381.5
Q368289.75
95-th percentile83927
Maximum666666
Range664936
Interquartile range (IQR)32751

Descriptive statistics

Standard deviation25037.956
Coefficient of variation (CV)0.47930563
Kurtosis161.40014
Mean52237.975
Median Absolute Deviation (MAD)16409
Skewness6.8009474
Sum1.1701306 × 108
Variance6.2689924 × 108
MonotonicityNot monotonic
2023-04-28T21:11:07.826179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51381.5 24
 
1.1%
7500 12
 
0.5%
35860 4
 
0.2%
37760 3
 
0.1%
83844 3
 
0.1%
63841 3
 
0.1%
18929 3
 
0.1%
47025 3
 
0.1%
34176 3
 
0.1%
48432 3
 
0.1%
Other values (1965) 2179
97.3%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

Dt_Customer
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
10
1189 
9
557 
11
494 

Length

Max length2
Median length2
Mean length1.7513393
Min length1

Characters and Unicode

Total characters3923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row9
3rd row10
4th row9
5th row9

Common Values

ValueCountFrequency (%)
10 1189
53.1%
9 557
24.9%
11 494
22.1%

Length

2023-04-28T21:11:08.013097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:08.201612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
10 1189
53.1%
9 557
24.9%
11 494
22.1%

Most occurring characters

ValueCountFrequency (%)
1 2177
55.5%
0 1189
30.3%
9 557
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3923
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2177
55.5%
0 1189
30.3%
9 557
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3923
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2177
55.5%
0 1189
30.3%
9 557
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2177
55.5%
0 1189
30.3%
9 557
 
14.2%

Recency
Real number (ℝ)

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.109375
Minimum0
Maximum99
Zeros28
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:08.367756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.962453
Coefficient of variation (CV)0.58975405
Kurtosis-1.2018968
Mean49.109375
Median Absolute Deviation (MAD)25
Skewness-0.0019866586
Sum110005
Variance838.82367
MonotonicityNot monotonic
2023-04-28T21:11:08.565001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.3%
49 30
 
1.3%
65 30
 
1.3%
3 29
 
1.3%
29 29
 
1.3%
71 29
 
1.3%
Other values (90) 1931
86.2%
ValueCountFrequency (%)
0 28
1.2%
1 24
1.1%
2 28
1.2%
3 29
1.3%
4 27
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 25
1.1%
95 19
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.3%
91 18
0.8%
90 20
0.9%

NumDealsPurchases
Real number (ℝ)

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.325
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:08.726387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9322375
Coefficient of variation (CV)0.83106989
Kurtosis8.9369143
Mean2.325
Median Absolute Deviation (MAD)1
Skewness2.4185694
Sum5208
Variance3.7335418
MonotonicityNot monotonic
2023-04-28T21:11:08.858082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
0 46
 
2.1%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 24
 
1.1%
ValueCountFrequency (%)
0 46
 
2.1%
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 4
 
0.2%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 40
1.8%
6 61
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0848214
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:08.997040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7787141
Coefficient of variation (CV)0.68025352
Kurtosis5.7031284
Mean4.0848214
Median Absolute Deviation (MAD)2
Skewness1.3827943
Sum9150
Variance7.7212523
MonotonicityNot monotonic
2023-04-28T21:11:09.139506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 373
16.7%
1 354
15.8%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
0 49
 
2.2%
Other values (5) 91
 
4.1%
ValueCountFrequency (%)
0 49
 
2.2%
1 354
15.8%
2 373
16.7%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.3%
8 102
4.6%
7 155
6.9%
6 205
9.2%
5 220
9.8%

NumCatalogPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6620536
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:09.268478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9231007
Coefficient of variation (CV)1.0980623
Kurtosis8.0474368
Mean2.6620536
Median Absolute Deviation (MAD)2
Skewness1.8809888
Sum5963
Variance8.5445174
MonotonicityNot monotonic
2023-04-28T21:11:09.378711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
10 48
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.8%
10 48
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.5%
6 128
5.7%
5 140
6.2%
4 182
8.1%

NumStorePurchases
Real number (ℝ)

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7901786
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:09.494498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2509581
Coefficient of variation (CV)0.56146077
Kurtosis-0.62204828
Mean5.7901786
Median Absolute Deviation (MAD)2
Skewness0.70223729
Sum12970
Variance10.568729
MonotonicityNot monotonic
2023-04-28T21:11:09.631257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 490
21.9%
4 323
14.4%
2 223
10.0%
5 212
9.5%
6 178
 
7.9%
8 149
 
6.7%
7 143
 
6.4%
10 125
 
5.6%
9 106
 
4.7%
12 105
 
4.7%
Other values (4) 186
 
8.3%
ValueCountFrequency (%)
0 15
 
0.7%
1 7
 
0.3%
2 223
10.0%
3 490
21.9%
4 323
14.4%
5 212
9.5%
6 178
 
7.9%
7 143
 
6.4%
8 149
 
6.7%
9 106
 
4.7%
ValueCountFrequency (%)
13 83
 
3.7%
12 105
 
4.7%
11 81
 
3.6%
10 125
 
5.6%
9 106
 
4.7%
8 149
6.7%
7 143
6.4%
6 178
7.9%
5 212
9.5%
4 323
14.4%

NumWebVisitsMonth
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3165179
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:09.766377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.426645
Coefficient of variation (CV)0.45643503
Kurtosis1.8216138
Mean5.3165179
Median Absolute Deviation (MAD)2
Skewness0.20792556
Sum11909
Variance5.888606
MonotonicityNot monotonic
2023-04-28T21:11:09.920735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 393
17.5%
8 342
15.3%
6 340
15.2%
5 281
12.5%
4 218
9.7%
3 205
9.2%
2 202
9.0%
1 153
 
6.8%
9 83
 
3.7%
0 11
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 11
 
0.5%
1 153
 
6.8%
2 202
9.0%
3 205
9.2%
4 218
9.7%
5 281
12.5%
6 340
15.2%
7 393
17.5%
8 342
15.3%
9 83
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 83
 
3.7%
8 342
15.3%
7 393
17.5%
6 340
15.2%

AcceptedCmp3
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Length

2023-04-28T21:11:10.080809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:10.233545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

AcceptedCmp4
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2073 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Length

2023-04-28T21:11:10.367189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:10.527822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

AcceptedCmp5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Length

2023-04-28T21:11:10.661795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:10.811148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

AcceptedCmp1
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2096 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Length

2023-04-28T21:11:10.941901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:11.108459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

AcceptedCmp2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2210 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Length

2023-04-28T21:11:11.228060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:11.384667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1906 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Length

2023-04-28T21:11:11.509320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:11.657892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Age
Real number (ℝ)

Distinct59
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.194196
Minimum27
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:11.828986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile35
Q146
median53
Q364
95-th percentile73
Maximum130
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.984069
Coefficient of variation (CV)0.22113197
Kurtosis0.71746444
Mean54.194196
Median Absolute Deviation (MAD)9
Skewness0.34994386
Sum121395
Variance143.61792
MonotonicityNot monotonic
2023-04-28T21:11:12.034540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 89
 
4.0%
52 87
 
3.9%
48 83
 
3.7%
51 79
 
3.5%
45 77
 
3.4%
53 77
 
3.4%
50 74
 
3.3%
58 74
 
3.3%
54 71
 
3.2%
49 69
 
3.1%
Other values (49) 1460
65.2%
ValueCountFrequency (%)
27 2
 
0.1%
28 5
 
0.2%
29 3
 
0.1%
30 5
 
0.2%
31 13
0.6%
32 15
0.7%
33 18
0.8%
34 30
1.3%
35 29
1.3%
36 27
1.2%
ValueCountFrequency (%)
130 1
 
< 0.1%
124 1
 
< 0.1%
123 1
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
80 7
0.3%
79 7
0.3%
78 8
0.4%
77 16
0.7%
76 16
0.7%

Total_Spending
Real number (ℝ)

Distinct1054
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.79821
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-04-28T21:11:12.249986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q168.75
median396
Q31045.5
95-th percentile1772.3
Maximum2525
Range2520
Interquartile range (IQR)976.75

Descriptive statistics

Standard deviation602.24929
Coefficient of variation (CV)0.99414174
Kurtosis-0.34193682
Mean605.79821
Median Absolute Deviation (MAD)353
Skewness0.86084051
Sum1356988
Variance362704.2
MonotonicityNot monotonic
2023-04-28T21:11:12.461836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 19
 
0.8%
22 18
 
0.8%
57 16
 
0.7%
44 15
 
0.7%
55 15
 
0.7%
48 14
 
0.6%
20 14
 
0.6%
43 14
 
0.6%
37 14
 
0.6%
38 14
 
0.6%
Other values (1044) 2087
93.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
8 4
 
0.2%
9 2
 
0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 10
0.4%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 2
0.1%
2283 1
< 0.1%
2279 1
< 0.1%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
1
1128 
0
638 
2
421 
3
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Length

2023-04-28T21:11:12.660815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T21:11:12.880749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Interactions

2023-04-28T21:11:04.652707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:50.655216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:52.109353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:53.647517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:55.299814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:57.397149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:59.065123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:00.787221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:03.178951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:04.829290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:50.810136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:52.257210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:53.825042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:55.494278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:57.597164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:59.292567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:01.013463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:03.336528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:05.001772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:50.958664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:52.426257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:53.994861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:55.659838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:57.780673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:59.505995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:01.278690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:03.491357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:05.182393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:51.143205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:52.608805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:54.178353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:55.947067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:57.963594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:59.698679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:01.953850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:03.663894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:05.340971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:51.299096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:52.771267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:54.356977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:56.353991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:58.159606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:59.862281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:02.127891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:03.818483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:05.550409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:51.474175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:52.927849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:54.536274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:56.563871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:58.316221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:00.042759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:02.363669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:03.978101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:05.715022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:51.645718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:53.109543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:54.726287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:56.792766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:58.493711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:00.260177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:02.654631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:04.147882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:05.897864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:51.823241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:53.304026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:54.926283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:57.029137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:58.704184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:00.450668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:02.859803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:04.326406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:06.050245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:51.963865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:53.472984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:55.121276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:57.213640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:10:58.881674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:00.615785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:03.020373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-28T21:11:04.489811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-04-28T21:11:13.099167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
IncomeRecencyNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAgeTotal_SpendingEducationMarital_StatusDt_CustomerAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseNumber_of_Children
Income1.0000.008-0.1930.5710.7880.729-0.6400.2160.8470.0510.0000.0000.0000.1360.4470.3580.0680.1930.303
Recency0.0081.0000.008-0.0040.0310.006-0.0220.0210.0200.0000.0470.0090.0440.0000.0000.0000.0340.2080.038
NumDealsPurchases-0.1930.0081.0000.284-0.0400.1000.3980.087-0.0150.0000.0220.1450.0000.0510.2450.1640.0000.0960.368
NumWebPurchases0.571-0.0040.2841.0000.6190.673-0.0970.1640.7270.0830.0120.1230.0240.1550.1710.1650.0000.1660.149
NumCatalogPurchases0.7880.031-0.0400.6191.0000.709-0.5360.1790.8920.0640.0220.0520.0890.1920.3590.3140.1110.2190.292
NumStorePurchases0.7290.0060.1000.6730.7091.000-0.4540.1680.8050.1000.0000.0790.1780.2130.2290.1980.0810.1490.200
NumWebVisitsMonth-0.640-0.0220.398-0.097-0.536-0.4541.000-0.131-0.4760.0410.0260.1990.0770.0000.3090.2020.0000.1210.325
Age0.2160.0210.0870.1640.1790.168-0.1311.0000.1570.1470.0770.0000.0520.0430.0930.0540.0000.0000.184
Total_Spending0.8470.020-0.0150.7270.8920.805-0.4760.1571.0000.0990.0000.1150.0560.2510.5260.4170.1510.2940.324
Education0.0510.0000.0000.0830.0640.1000.0410.1470.0991.0000.0000.0350.0000.0490.0360.0180.0000.0810.034
Marital_Status0.0000.0470.0220.0120.0220.0000.0260.0770.0000.0001.0000.0000.0000.0000.0000.0000.0000.1470.047
Dt_Customer0.0000.0090.1450.1230.0520.0790.1990.0000.1150.0350.0001.0000.0000.0190.0000.0230.0000.1810.015
AcceptedCmp30.0000.0440.0000.0240.0890.1780.0770.0520.0560.0000.0000.0001.0000.0730.0740.0890.0610.2510.000
AcceptedCmp40.1360.0000.0510.1550.1920.2130.0000.0430.2510.0490.0000.0190.0731.0000.3030.2470.2840.1730.085
AcceptedCmp50.4470.0000.2450.1710.3590.2290.3090.0930.5260.0360.0000.0000.0740.3031.0000.3990.2130.3240.347
AcceptedCmp10.3580.0000.1640.1650.3140.1980.2020.0540.4170.0180.0000.0230.0890.2470.3991.0000.1660.2910.278
AcceptedCmp20.0680.0340.0000.0000.1110.0810.0000.0000.1510.0000.0000.0000.0610.2840.2130.1661.0000.1630.073
Response0.1930.2080.0960.1660.2190.1490.1210.0000.2940.0810.1470.1810.2510.1730.3240.2910.1631.0000.204
Number_of_Children0.3030.0380.3680.1490.2920.2000.3250.1840.3240.0340.0470.0150.0000.0850.3470.2780.0730.2041.000

Missing values

2023-04-28T21:11:06.327494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-28T21:11:06.731565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EducationMarital_StatusIncomeDt_CustomerRecencyNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseAgeTotal_SpendingNumber_of_Children
0GraduationSingle58138.011583810470000016616170
1GraduationSingle46344.09382112500000069272
2GraduationNot Single71613.01026182104000000587760
3GraduationNot Single26646.09262204600000039531
4Post GraduateNot Single58293.099455365000000424221
5Post GraduateNot Single62513.01016264106000000567161
6GraduationSingle55635.0113447376000000525901
7Post GraduateNot Single33454.0103224048000000381691
8Post GraduateNot Single30351.010191302900000149461
9Post GraduateNot Single5648.096811002010000073492
EducationMarital_StatusIncomeDt_CustomerRecencyNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseAgeTotal_SpendingNumber_of_Children
2230GraduationSingle11012.010823312910000039841
2231Post GraduateSingle44802.011712941280000005310490
2232GraduationSingle26816.011501003400000037220
2233GraduationNot Single666666.010234313600000046621
2234GraduationNot Single34421.010811102700000049301
2235GraduationNot Single61223.01046293450000005613411
2236Post GraduateNot Single64014.095678257000100774443
2237GraduationSingle56981.09911231360100004212410
2238Post GraduateNot Single69245.098265103000000678431
2239Post GraduateNot Single52869.0114033147000001691722

Duplicate rows

Most frequently occurring

EducationMarital_StatusIncomeDt_CustomerRecencyNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseAgeTotal_SpendingNumber_of_Children# duplicates
0GraduationNot Single18690.0117711128000000646003
1GraduationNot Single18929.0101511046000000338503
24GraduationNot Single39922.01030230480000004015613
44GraduationNot Single67445.0116359612600000049117413
58GraduationNot Single83844.0105714411100100071157403
150Post GraduateSingle63841.01064193960000005590813
2GraduationNot Single19986.0107410037000000382212
3GraduationNot Single21994.011410035000000662212
4GraduationNot Single22419.01074132280000006016202
5GraduationNot Single22574.0102822037000000563732